About

Imported from borghei/claude-skills (engineering/chaos-engineering/SKILL.md). Install upstream with npx skills add borghei/claude-skills --skill chaos-engineering. Copyright stays with the author (MIT + Commons Clause).

Chaos Engineering

Name: chaos-engineering
Author: borghei

End-to-end chaos engineering: experiment design, fault injection catalog, gameday execution, and the maturity model that turns one-off "let's break stuff" exercises into a reliable discipline. Provider-agnostic — works whether you use Litmus, Chaos Mesh, AWS FIS, Gremlin, ChaosToolkit, or hand-rolled scripts.

This skill answers four questions: what to inject, where to inject it, how to size the blast, and how to extract durable learning from each run.

Core Capabilities

Principles & maturity — the five Principles of Chaos and a four-level maturity model (L0 none → L4 always-on production chaos) with level-up criteria.
Experiment design loop — a nine-step loop (steady state → hypothesis → variables → blast radius → abort → run → analyze → act → document) with worked good/bad examples.
Fault catalog — what to inject per layer: pod/host, network, dependency, resource, state, and traffic, with tool mappings.
Blast-radius sizing — quantify worst-case affected users and recommended caps; experiments start tiny (1 pod / 1% / 1 min) and grow only after passing.
Gameday execution — scheduled multi-scenario exercises with roles, agendas, scenario selection, and debrief templates.
Discipline — anti-patterns to avoid, the "first five experiments" for new teams, and end-to-end workflows (single experiment, gameday, kill-switch verification, post-incident verification).

When to Use

Situation	Skill applies
Spinning up a chaos program from scratch	Yes — start with maturity model + first 5 experiments
Designing a single experiment for a known concern	Yes — use the experiment design loop
Planning a gameday for a team or service	Yes — use `scripts/gameday_planner.py`
Validating a kill switch or fallback path actually works	Yes — chaos is the way to test these in prod-like conditions
Post-incident verification: "did the fix really fix it?"	Yes — re-inject the original fault, confirm the new behavior
Compliance evidence (SOC 2 A1 / DORA Art. 25)	Yes — chaos runs produce auditable resilience-testing evidence
Improving SLOs / error budgets	Pair with `engineering/observability-designer` — chaos surfaces SLO violations

Clarify First

Before designing the experiment, confirm these inputs. If any is unknown or vague, ASK — do not assume:

Target & fault type — the service and what to inject (dependency-timeout, network, pod-kill, resource exhaustion) (drives the experiment doc via --target/--fault)
Steady-state hypothesis — the metric that defines "healthy" and the expected behavior under fault (the hypothesis the experiment tests)
Blast radius caps — user count, % targeted, duration, and abort triggers (sizes the experiment via blast_radius_calculator.py)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

Tool	Purpose	Command
`chaos_experiment_designer.py`	Scaffold a chaos experiment doc (hypothesis stub, steady-state template, abort criteria, checklist)	`python scripts/chaos_experiment_designer.py --target payments-svc --fault dependency-timeout --duration 5m`
`blast_radius_calculator.py`	Compute worst-case affected users, recommended caps, and abort triggers	`python scripts/blast_radius_calculator.py --users 100000 --percent-targeted 1 --duration 60`
`gameday_planner.py`	Generate a tailored gameday agenda with roles, timeline, and debrief template	`python scripts/gameday_planner.py --service search-api --duration full --scenarios region-failover,dep-outage`

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

references/experiment-design-loop.md — the five principles, the four-level maturity model, the full nine-step design loop with worked steady-state/hypothesis/abort examples, and the "first five experiments." Read when designing or running an experiment.
references/gameday-workflows-and-antipatterns.md — the gameday agenda and roles, scenario selection, the chaos anti-patterns, all four end-to-end workflows, and the script tooling-output table. Read when planning a gameday or running a workflow.
references/chaos-principles-and-maturity.md — per-level maturity scorecard, the L0→L1 "first five experiments" list, and the org/SRE prerequisites for each level. Read when assessing or leveling up a program.
references/gameday-playbook.md — 12 scenario templates by domain (API service, database, frontend, async pipeline, multi-region, etc.), half-day/full-day agendas, and post-gameday writeup templates. Read when choosing gameday scenarios.
references/fault-injection-catalog.md — fault types per layer (network, host, dependency, resource, state, traffic) with tool mappings (Chaos Mesh / Litmus / AWS FIS / Gremlin / ChaosToolkit). Read when choosing what to inject.

Related skills

engineering/observability-designer — wire metrics needed to define steady state
engineering/incident-commander — chaos is rehearsal for the incident response you'll need
engineering/feature-flags-architect — kill switches verified by chaos; chaos verified by flags
ra-qm-team/dora-compliance-expert — DORA Article 25 requires resilience testing; chaos runs are evidence

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/borghei-claude-skills-chaos-engineering/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

borghei-claude-skills-chaos-engineering.ocm.jsonjson

{
  "ocm": "1",
  "id": "borghei-claude-skills-chaos-engineering",
  "kind": "skill",
  "name": "chaos-engineering",
  "description": "Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.",
  "publisher": "borghei",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "chaos-engineering",
      "resilience",
      "fault-injection",
      "gameday",
      "sre",
      "reliability",
      "blast-radius",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/borghei/claude-skills",
      "path": "engineering/chaos-engineering/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/borghei/claude-skills/blob/HEAD/engineering/chaos-engineering/SKILL.md",
      "key": "borghei/claude-skills/engineering/chaos-engineering/SKILL.md"
    },
    "license": "MIT + Commons Clause"
  },
  "instructions": "# Chaos Engineering\n\nEnd-to-end chaos engineering: experiment design, fault injection catalog, gameday execution, and the maturity model that turns one-off \"let's break stuff\" exercises into a reliable discipline. Provider-agnostic — works whether you use Litmus, Chaos Mesh, AWS FIS, Gremlin, ChaosToolkit, or hand-rolled scripts.\n\nThis skill answers four questions: **what to inject, where to inject it, how to size the blast, and how to extract durable learning** from each run.\n\n## Core Capabilities\n\n- **Principles & maturity** — the five Principles of Chaos and a four-level maturity model (L0 ",
  "cost": {
    "context_tokens": 1425
  }
}

Fetch it by URL: GET /api/v1/registry/borghei-claude-skills-chaos-engineering/manifest?version=1.0.0

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